محمدباقر زندی

Genetic evaluation of sport horses using various methods is commonly carried out in many countries, except for Iran. Accurate estimation of genetic parameters for functional traits in horses has always been a subject of interest among livestock breeders. The aim of this study was to investigate the genetic parameters of athletic performance traits in Iranian jumping horses. Traits include race completion time (RCT), rank at the end of the competition (REC), and number of errors in the competition (NEC). The study were conducted based on Equestrian Federation of Iran (FEI) athletic performance records of 1498 horses (10–23 years old). The genetic parameters were estimated by using Restricted Maximum Likelihood (REML) procedures and airemf90 software. The statistical model included fixed effects of birth year, sex, age, breed, height of obstacles, date/city, level of difficulty of the event and random effects of rider, permanent environment and an additive genetic effect. Heritability of RCT, REC and NEC traits were estimated 0.03, 0.11 and 0.09 and their repeatability were 0.16, 0.25 and 0.13, respectively. Genetic and phenotypic trends of RCT, REC and NEC traits were estimated -0.002, 0.0091, 0.0003 and -0.7057, 0.2575, -0.0807, respectively. According to this result, the performance traits are highly affected by environmental factors, especially the effect of the rider, but among the studied traits, selection based on rank traits can lead to more genetic progress. It can be concluded that we can apply these parameters to improve the genetic potential of show jumping performance records and design the appropriate breeding strategies for horse jumping population in Iran.

According to the World Anti-Doping Agency (WADA), gene doping refers to the non-therapeutic use of genes, gene components, and cellular elements that enhance the athletic performance capacity of animals. Doping used to be done with various drugs such as Anabolic Androgenic Steroids (AAS), Caffeine, Cocaine, Amphetamine, Apomorphine, Fentanyl, Barbiturates, Butazone, etc. but with the progress in the field of gene therapy, the necessary tools for gene doping were created. The objectives of this review were to introduce the types of drug doping, gene doping, the detection methods and important candidate genes used in gene doping in horses. Gene Doping Methods and Detection TechniquesGene doping methods can generally be classified into three categories: gene transfer, gene silencing, and gene editing. Various methods have been proposed for detecting gene doping, which can be broadly categorized as indirect and direct detection methods. Indirect methods measure the body's responses to doping, while direct method, which clearly identifies the doping agent, the goal is to identify the genetic materials of doping, a protein produced from the gene doping gene, or a vector. All gene doping detection methods have their advantages and disadvantages, and it is evident that detecting gene doping will be challenging. Candidate Genes in Gene Doping Candidate genes commonly used in gene doping include EPO, IGF1, GH, HIF1, PPARD, MSTN, ACTN2, ACTN3, VEGFA, POMC, PENK, ACE, and PCK1.

Given the sensitivity and significance of equestrian sports and horse racing, the likelihood of owners, trainers, and riders resorting to doping to achieve victory is increasing. As gene doping is more effective and harder to detect than traditional drug doping, it receives more attention from offenders. As a result, equestrian and horse racing competitions are facing a new challenge known as gene doping. Although there is currently no evidence of gene doping being used, the necessary technology exists to implement various forms of gene doping. The most effective measure for controlling doping is the introduction of precise doping detection tests. While a wide range of diagnostic tests has been proposed, there is currently no official and widespread method for detecting gene doping. A successful anti-doping program should not only include diagnostic tests but also focus on education, supervision, and implementation methods.

Conservation of the genetic diversity of indigenous animals is very important. For the sustainable use of genetic resources, it is necessary to first study the genetic structure of populations. The main goals of this research were to identify the population structure of Turkmen and Darehshori horses using dense SNP markers and to compare the effectiveness of PCA, DAPC, and SPC methods in clustering these populations.

For this purpose, 67 Turkmen and 39 Darehshori horses were genotyped using Illumina EquineSNP70 BeadChip. After applying quality control steps, five Turkmen horses and one Darehshori horse were removed. Then, the structure of populations was identified by three methods of principal component analysis (PCA), discriminant analysis of principal components (DAPC), and superparamagnetic clustering (SPC). These methods do not depend on previous assumptions and make it possible to analyze very large genome databases without prior knowledge of individual ancestry. These methods are also very fast and efficient.

This study compared the efficiency of these three clustering methods in identifying population structures. All three methods were successful in separating the two breeds, and Turkmen and Darehshori breeds were grouped into separate genetic groups. The difference is that the DAPC method only separated the two main populations, but the PCA and SPC methods could identify several subpopulations in each breed. The results of this study showed that the SPC method for studying the population structure of indigenous breeds with unknown information can be more useful than other methods. Therefore, using this method, a suitable program can be designed to conserve and use genetic resources.

PCA, DAPC, and SPC methods were able to successfully identify the genetic structure of Turkmen and Darehshori breeds, and in general, it can be said that the information obtained from dense SNP markers can be a powerful tool for identifying the population structure of indigenous breeds.

The aim of this study was to investigate the genetic structure of Iranian native horse breeds to compare genetic diversity and understanding the relationships between the populations using 11 ISAG microsatellite markers. For this reason, 565 samples of the Iranian Equestrian database from different ages including the Iranian Arab Asil, Caspian, Darehshouri, Kurdish and Turkmen and 113 samples were used for each breed. The number of observed alleles for each locus was 7 to 19 alleles with an average of 10.81 alleles, and it was 19 for ASB17 and 7 for HTG4 locus with the highest and lowest observed alleles ranking respectively. The average observed heterozygosity from the largest to the lowest rank was, Turkmen (0.68±0.11), Caspian (0.67±0.07), Kurdish (0.66±0.06) Darehshouri (0.65±0.07), and Arab Asil (0.62±0.08). Population structure analysis with UPGMA method showed that Caspian and Kurdish populations were grouped as a unit cluster while the other populations grouped as a separate cluster. These results confirmed this hypothesis that the Caspian and Kurdish populations are close to the Nisa horses. In general, the results of this study indicate that the Iranian native horses have got a high genetic diversity, despite of populations have genetic similarity and the other hand genetic clustering of the populations is consistent with their geographic distances. The result of this study shows that the ISAG microsatellite markers are polymorphic and have more efficiency for assignment genetic diversity and genetic structure analysis of Iranian native horse breeds.

Identification of genomic regions controlling economic traits is one of the most challenging uses of dense markers in animal genetics. Fat storage is economically important for breeding sheep. Therefore, identification of lipid-associated gene positions was the main goals of this study.In this study, 49034 Single Nucleotide Polymorphisms (SNPs) were used to identify the genomic regions affecting fat deposition of 106 sheep individuals include Afshari (n=37), Moghani (n=34) and Qezel (n=35) breeds were used. All samples were genotyped by Illumina OvineSNP50K bead chip. By running the quality control on the raw genotype, from tolal 49,034 SNP markers only 46,676 SNPs remained then genomic relationship principal component analysis (PCA) was used in order to assign correct individual to each breed. Theta and Fst analysis were applied to survey the population differentiation in three breeds, so five regions in 99.99 percentile FST score of the genome distribution were selected for further analysis. These regions are stated on chromosomes number 3, 13 (two areas), 15 and 22 were associated with LDL and HDL receptors Lipolise regulation in fat cells and fatty acid metolism. To verify those regions, Extended Haplotype Homozygosity (EHH) test based on linkage disequilibrium was perform. The results of this study shows that SNP bead chip are useful for population differentiation and GWAS so this can help us to achive an genetic improvement by genomic selection.

Genomic Selection (GS) is a tool for prediction of breeding values for quantitative traits. For a successful application of GS, accuracy of predicted genomic breeding value (GEBV) is a key issue to consider. Here we investigated the accuracy of GEBV in 345 genotyped Iranian Holstein cattle. The study was performed on milk, fat, protein yield and somatic cell count. Four methods G-BLUP, Bayes B, Reproducing kernel Hilbert Spaces (RKHS) and Neural Networks (NN) were used to predict genomic breeding values and their accuracies. The GEBV accuracies varied between 0.39 for somatic cell count to 0.73 for fat yield. Bayes B gave the highest accuracies among methods. Bayes B and non- parametric methods tended to produce inflated predictions ( slope of the regression of GBV on EBV greater than 1). However, in all traits, lower estimates of MSE were obtained using G- BLUP. Bayes B regression model are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions.

The objective followed in this study was to compare six software packages (DFREML, WOMBAT, ASReml, MATVEC, DMU and Thrgibbsf90) as based on some such features as being user friendly, easing computational facilities as well as accuracy and precision of the estimated genetic vs. environmental parameters. For conduction of the study, simulated and field data (Birth weights and weaning weight of Baluchi lambs) were utilized. In all the softwares, except for Thrgibbsf 90, estimates were obtained with restricted Maximum likelihood method via AI algorithm, but in Thrgibbsf 90, the estimates were obtained using Bayesian Gibbs Sampling. The results showed that the choice of software depends on user interest and the goal followed in the research. Since the estimated parameters come out to be similar, even in confidence intervals, there seems no concern to exist in estimation of genetic and environmental parameters (for continous variables) using linear models,and through an application of different softwares.